1. Technical Field
The present invention relates to fingerprint processing and more particularly to a system and method for employing Gabor coefficients in a texture representation for fingerprint recognition.
2. Description of the Related Art
Biometric identifiers, such as, fingerprints, face, iris and voice prints, offer a way of reliable personal authentication. Biometrics is rapidly replacing traditional token and password methods. Of all the biometric modalities, fingerprints have emerged as a popular choice due to their universality, distinctiveness, permanence and acceptability. Another reason for their popularity is the wide variety of implementations of recognition algorithms that are already available.
Existing fingerprint matching algorithms may be broadly classified into the following categories based on fingerprint representation.
1. Correlation based: In this representation, the fingerprint image itself is used as a template. Matching is performed by measuring the result of cross correlation between the two images. This requires reasonably low resolution images and is very fast, since correlation may also be implemented through optical techniques. However, the matching is global and requires an accurate registration of the target and reference fingerprint images, since correlation is not invariant to translation and rotation. The accuracy of correlation based techniques further degrades with non-linear distortion of the fingerprint.
2. Minutiae Representation: Minutiae represent local fingerprint ridge discontinuities and mark the position where a ridge comes to an end or bifurcates into two. Given target and reference fingerprints and their corresponding minutiae features, the process of matching is a point pattern matching problem. This is by far the most popular approach to fingerprint recognition. However, minutiae based matching algorithms do not perform well in the case of small fingerprints that have very few minutiae. Furthermore, minutiae based systems completely ignore gray scale content of the fingerprint images. Human experts routinely utilize the rich structural and texture cues present in the fingerprint image during the process of matching. Minutiae based representations do not encode this information either explicitly or implicitly.
3. Texture Descriptors: A fingerprint image can also be viewed as a pattern of oriented texture formed by the gray scale variation of the ridges. Therefore, texture descriptors provide a good representation for the ridge content in the image. A global texture descriptor scheme called ‘finger code’ utilizes both global and local ridge descriptions. The features are extracted by measuring the responses of radial fingerprint image sectors to a filterbank. The matching is based on measuring the Euclidean distance between the feature vectors. A disadvantage of this approach is that it requires that the fingerprint core is accurately located. This is a difficult problem in itself.
Thus, algorithms based on minutiae require a point matching algorithm, but do not measure gray scale content. On the other hand, correlation and texture based methods measure gray scale content but require very accurate alignment. Another issue with the above mentioned techniques is that of scalability. It has been shown that algorithms designed for 1:1 verification scale poorly when used for 1:N identification tasks.
Fingerprint recognition based on localized information selects ‘interesting’ regions in the fingerprint image to perform local gray scale correlation. Plain ridges do not carry any information except their orientation, ridge frequency, ridge endings and ridge bifurcations. The ‘interesting’ regions (similar to distinctive minutiae configurations) include regions around the minutiae, regions of high curvature and regions around the singular points such as core and delta. However, the optimal process of selecting these ‘interesting’ regions is very inefficient. Furthermore, the algorithm is not robust to rotations. Localized correlation is used in other methods, but only in conjunction with a geometry based minutiae matcher. The localized correlation is used to assess the quality of the pairs obtained by the minutiae matcher. The localized correlation itself is not accurate enough to be useful. Furthermore, the algorithms are designed for 1:1 verification and are therefore not directly scalable for large scale identification tasks.